Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory450.7 B

Variable types

Numeric6
Categorical5
DateTime1

Alerts

Amount is highly overall correlated with Total_Value and 1 other fieldsHigh correlation
Price_Per_Unit is highly overall correlated with Total_Value and 1 other fieldsHigh correlation
Total_Value is highly overall correlated with Amount and 2 other fieldsHigh correlation
Transaction_Fee is highly overall correlated with Amount and 2 other fieldsHigh correlation
Total_Value has unique valuesUnique

Reproduction

Analysis started2025-05-24 14:37:59.041596
Analysis finished2025-05-24 14:38:00.595935
Duration1.55 second
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Transaction_ID
Real number (ℝ)

Distinct9946
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547132.39
Minimum100027
Maximum999919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-05-24T20:08:00.622171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum100027
5-th percentile143551.4
Q1318120.75
median547635
Q3775336
95-th percentile953077.55
Maximum999919
Range899892
Interquartile range (IQR)457215.25

Descriptive statistics

Standard deviation262163.28
Coefficient of variation (CV)0.47915876
Kurtosis-1.2172722
Mean547132.39
Median Absolute Deviation (MAD)228824.5
Skewness0.0053535418
Sum5.4713239 × 109
Variance6.8729584 × 1010
MonotonicityNot monotonic
2025-05-24T20:08:00.764461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701012 3
 
< 0.1%
186612 2
 
< 0.1%
432981 2
 
< 0.1%
930644 2
 
< 0.1%
986417 2
 
< 0.1%
551164 2
 
< 0.1%
375461 2
 
< 0.1%
148659 2
 
< 0.1%
447010 2
 
< 0.1%
859025 2
 
< 0.1%
Other values (9936) 9979
99.8%
ValueCountFrequency (%)
100027 1
< 0.1%
100089 1
< 0.1%
100213 1
< 0.1%
100278 1
< 0.1%
100329 1
< 0.1%
100346 1
< 0.1%
100465 1
< 0.1%
100646 1
< 0.1%
100652 1
< 0.1%
100676 1
< 0.1%
ValueCountFrequency (%)
999919 1
< 0.1%
999907 1
< 0.1%
999589 1
< 0.1%
999488 1
< 0.1%
999398 1
< 0.1%
999365 1
< 0.1%
999312 1
< 0.1%
999298 1
< 0.1%
999250 1
< 0.1%
999138 1
< 0.1%

User_ID
Real number (ℝ)

Distinct9418
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55176.38
Minimum10016
Maximum99986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-05-24T20:08:00.799640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10016
5-th percentile14603.05
Q132775
median55643
Q377450
95-th percentile95200.35
Maximum99986
Range89970
Interquartile range (IQR)44675

Descriptive statistics

Standard deviation25823.905
Coefficient of variation (CV)0.46802463
Kurtosis-1.1856126
Mean55176.38
Median Absolute Deviation (MAD)22357.5
Skewness-0.018434948
Sum5.517638 × 108
Variance6.6687407 × 108
MonotonicityNot monotonic
2025-05-24T20:08:00.832206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31118 4
 
< 0.1%
88463 3
 
< 0.1%
30825 3
 
< 0.1%
49954 3
 
< 0.1%
90456 3
 
< 0.1%
86142 3
 
< 0.1%
73304 3
 
< 0.1%
83160 3
 
< 0.1%
38331 3
 
< 0.1%
60349 3
 
< 0.1%
Other values (9408) 9969
99.7%
ValueCountFrequency (%)
10016 1
< 0.1%
10031 1
< 0.1%
10034 1
< 0.1%
10039 1
< 0.1%
10054 1
< 0.1%
10084 1
< 0.1%
10088 1
< 0.1%
10091 1
< 0.1%
10096 1
< 0.1%
10117 1
< 0.1%
ValueCountFrequency (%)
99986 1
< 0.1%
99983 1
< 0.1%
99982 1
< 0.1%
99974 1
< 0.1%
99958 1
< 0.1%
99957 1
< 0.1%
99953 2
< 0.1%
99946 1
< 0.1%
99938 1
< 0.1%
99934 1
< 0.1%

Crypto
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size622.5 KiB
Polkadot
1077 
Ethereum
1042 
Avalanche
1009 
Solana
1000 
Cardano
997 
Other values (5)
4875 

Length

Max length9
Median length8
Mean length6.7336
Min length3

Characters and Unicode

Total characters67336
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBitcoin
2nd rowSolana
3rd rowCardano
4th rowXRP
5th rowDogecoin

Common Values

ValueCountFrequency (%)
Polkadot 1077
10.8%
Ethereum 1042
10.4%
Avalanche 1009
10.1%
Solana 1000
10.0%
Cardano 997
10.0%
Dogecoin 997
10.0%
Bitcoin 991
9.9%
BNB 973
9.7%
XRP 964
9.6%
Litecoin 950
9.5%

Length

2025-05-24T20:08:00.864657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T20:08:00.900709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
polkadot 1077
10.8%
ethereum 1042
10.4%
avalanche 1009
10.1%
solana 1000
10.0%
cardano 997
10.0%
dogecoin 997
10.0%
bitcoin 991
9.9%
bnb 973
9.7%
xrp 964
9.6%
litecoin 950
9.5%

Most occurring characters

ValueCountFrequency (%)
o 8086
12.0%
a 7089
 
10.5%
n 5944
 
8.8%
e 5040
 
7.5%
i 4879
 
7.2%
t 4060
 
6.0%
c 3947
 
5.9%
l 3086
 
4.6%
B 2937
 
4.4%
d 2074
 
3.1%
Other values (17) 20194
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53462
79.4%
Uppercase Letter 13874
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8086
15.1%
a 7089
13.3%
n 5944
11.1%
e 5040
9.4%
i 4879
9.1%
t 4060
7.6%
c 3947
7.4%
l 3086
 
5.8%
d 2074
 
3.9%
h 2051
 
3.8%
Other values (6) 7206
13.5%
Uppercase Letter
ValueCountFrequency (%)
B 2937
21.2%
P 2041
14.7%
E 1042
 
7.5%
A 1009
 
7.3%
S 1000
 
7.2%
C 997
 
7.2%
D 997
 
7.2%
N 973
 
7.0%
X 964
 
6.9%
R 964
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 67336
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8086
12.0%
a 7089
 
10.5%
n 5944
 
8.8%
e 5040
 
7.5%
i 4879
 
7.2%
t 4060
 
6.0%
c 3947
 
5.9%
l 3086
 
4.6%
B 2937
 
4.4%
d 2074
 
3.1%
Other values (17) 20194
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8086
12.0%
a 7089
 
10.5%
n 5944
 
8.8%
e 5040
 
7.5%
i 4879
 
7.2%
t 4060
 
6.0%
c 3947
 
5.9%
l 3086
 
4.6%
B 2937
 
4.4%
d 2074
 
3.1%
Other values (17) 20194
30.0%

Transaction_Type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size611.1 KiB
Stake
2052 
Sell
2036 
Buy
2008 
Transfer
1972 
Withdraw
1932 

Length

Max length8
Median length5
Mean length5.566
Min length3

Characters and Unicode

Total characters55660
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWithdraw
2nd rowStake
3rd rowBuy
4th rowStake
5th rowStake

Common Values

ValueCountFrequency (%)
Stake 2052
20.5%
Sell 2036
20.4%
Buy 2008
20.1%
Transfer 1972
19.7%
Withdraw 1932
19.3%

Length

2025-05-24T20:08:00.937389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T20:08:00.966301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
stake 2052
20.5%
sell 2036
20.4%
buy 2008
20.1%
transfer 1972
19.7%
withdraw 1932
19.3%

Most occurring characters

ValueCountFrequency (%)
e 6060
 
10.9%
a 5956
 
10.7%
r 5876
 
10.6%
S 4088
 
7.3%
l 4072
 
7.3%
t 3984
 
7.2%
k 2052
 
3.7%
B 2008
 
3.6%
u 2008
 
3.6%
y 2008
 
3.6%
Other values (9) 17548
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45660
82.0%
Uppercase Letter 10000
 
18.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6060
13.3%
a 5956
13.0%
r 5876
12.9%
l 4072
8.9%
t 3984
8.7%
k 2052
 
4.5%
u 2008
 
4.4%
y 2008
 
4.4%
s 1972
 
4.3%
f 1972
 
4.3%
Other values (5) 9700
21.2%
Uppercase Letter
ValueCountFrequency (%)
S 4088
40.9%
B 2008
20.1%
T 1972
19.7%
W 1932
19.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 55660
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6060
 
10.9%
a 5956
 
10.7%
r 5876
 
10.6%
S 4088
 
7.3%
l 4072
 
7.3%
t 3984
 
7.2%
k 2052
 
3.7%
B 2008
 
3.6%
u 2008
 
3.6%
y 2008
 
3.6%
Other values (9) 17548
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6060
 
10.9%
a 5956
 
10.7%
r 5876
 
10.6%
S 4088
 
7.3%
l 4072
 
7.3%
t 3984
 
7.2%
k 2052
 
3.7%
B 2008
 
3.6%
u 2008
 
3.6%
y 2008
 
3.6%
Other values (9) 17548
31.5%

Amount
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.120755
Minimum0.024363
Maximum99.997361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-05-24T20:08:00.998317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.024363
5-th percentile4.7611608
Q124.889845
median49.887958
Q375.319944
95-th percentile94.845563
Maximum99.997361
Range99.972998
Interquartile range (IQR)50.430099

Descriptive statistics

Standard deviation28.902577
Coefficient of variation (CV)0.57665885
Kurtosis-1.2112243
Mean50.120755
Median Absolute Deviation (MAD)25.172042
Skewness-0.0058524416
Sum501207.55
Variance835.35895
MonotonicityNot monotonic
2025-05-24T20:08:01.032883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.734205 2
 
< 0.1%
57.695145 1
 
< 0.1%
24.383327 1
 
< 0.1%
1.36796 1
 
< 0.1%
93.092645 1
 
< 0.1%
30.25636 1
 
< 0.1%
25.868743 1
 
< 0.1%
21.820794 1
 
< 0.1%
82.373269 1
 
< 0.1%
89.995432 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
0.024363 1
< 0.1%
0.025643 1
< 0.1%
0.03062 1
< 0.1%
0.042946 1
< 0.1%
0.04467 1
< 0.1%
0.059165 1
< 0.1%
0.063503 1
< 0.1%
0.086032 1
< 0.1%
0.106725 1
< 0.1%
0.117149 1
< 0.1%
ValueCountFrequency (%)
99.997361 1
< 0.1%
99.956675 1
< 0.1%
99.933502 1
< 0.1%
99.92635 1
< 0.1%
99.854893 1
< 0.1%
99.851535 1
< 0.1%
99.840552 1
< 0.1%
99.840023 1
< 0.1%
99.838614 1
< 0.1%
99.829637 1
< 0.1%

Price_Per_Unit
Real number (ℝ)

Distinct9993
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29888.221
Minimum10.27
Maximum59995.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-05-24T20:08:01.067212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10.27
5-th percentile2849.7
Q115240.167
median29823.745
Q344728.79
95-th percentile56810.666
Maximum59995.76
Range59985.49
Interquartile range (IQR)29488.623

Descriptive statistics

Standard deviation17215.203
Coefficient of variation (CV)0.57598621
Kurtosis-1.1866175
Mean29888.221
Median Absolute Deviation (MAD)14727.885
Skewness0.0085864853
Sum2.9888221 × 108
Variance2.9636321 × 108
MonotonicityNot monotonic
2025-05-24T20:08:01.100598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26830.29 2
 
< 0.1%
1711.76 2
 
< 0.1%
33503.64 2
 
< 0.1%
38522.52 2
 
< 0.1%
731.22 2
 
< 0.1%
34272.22 2
 
< 0.1%
13219.47 2
 
< 0.1%
48798.51 1
 
< 0.1%
3717.6 1
 
< 0.1%
44225.7 1
 
< 0.1%
Other values (9983) 9983
99.8%
ValueCountFrequency (%)
10.27 1
< 0.1%
23.54 1
< 0.1%
26.49 1
< 0.1%
29.45 1
< 0.1%
29.57 1
< 0.1%
30.56 1
< 0.1%
37.4 1
< 0.1%
37.82 1
< 0.1%
42.12 1
< 0.1%
52.02 1
< 0.1%
ValueCountFrequency (%)
59995.76 1
< 0.1%
59991.64 1
< 0.1%
59987.81 1
< 0.1%
59985.75 1
< 0.1%
59984.31 1
< 0.1%
59973.95 1
< 0.1%
59947.98 1
< 0.1%
59941.43 1
< 0.1%
59940.49 1
< 0.1%
59939.11 1
< 0.1%

Total_Value
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506658.1
Minimum36.65
Maximum5881193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-05-24T20:08:01.137157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum36.65
5-th percentile50878.482
Q1412410.73
median1124990.4
Q32310646.1
95-th percentile4229275.4
Maximum5881193
Range5881156.4
Interquartile range (IQR)1898235.4

Descriptive statistics

Standard deviation1327017.1
Coefficient of variation (CV)0.88076857
Kurtosis0.075487503
Mean1506658.1
Median Absolute Deviation (MAD)838300.97
Skewness0.95496889
Sum1.5066581 × 1010
Variance1.7609744 × 1012
MonotonicityNot monotonic
2025-05-24T20:08:01.170777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2610634.92 1
 
< 0.1%
94764.09 1
 
< 0.1%
346081.22 1
 
< 0.1%
1338108.7 1
 
< 0.1%
739154.84 1
 
< 0.1%
77901.33 1
 
< 0.1%
2910698.18 1
 
< 0.1%
4391642.99 1
 
< 0.1%
409403.62 1
 
< 0.1%
40666.21 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
36.65 1
< 0.1%
109.42 1
< 0.1%
170.9 1
< 0.1%
194.8 1
< 0.1%
243.54 1
< 0.1%
255.97 1
< 0.1%
285.51 1
< 0.1%
325.95 1
< 0.1%
373.82 1
< 0.1%
409.87 1
< 0.1%
ValueCountFrequency (%)
5881193.05 1
< 0.1%
5825412.37 1
< 0.1%
5777447.15 1
< 0.1%
5774439.13 1
< 0.1%
5754781.82 1
< 0.1%
5745719.43 1
< 0.1%
5727474.74 1
< 0.1%
5709680.9 1
< 0.1%
5701555.8 1
< 0.1%
5696869.62 1
< 0.1%

Transaction_Fee
Real number (ℝ)

Distinct9981
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37555.081
Minimum0.04
Maximum287527.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-05-24T20:08:01.206737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile582.689
Q16007.9775
median21182.08
Q353563.732
95-th percentile130800.87
Maximum287527.83
Range287527.79
Interquartile range (IQR)47555.755

Descriptive statistics

Standard deviation43871.976
Coefficient of variation (CV)1.1682035
Kurtosis3.456876
Mean37555.081
Median Absolute Deviation (MAD)17927.245
Skewness1.8108568
Sum3.7555081 × 108
Variance1.9247503 × 109
MonotonicityNot monotonic
2025-05-24T20:08:01.241881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10520.9 2
 
< 0.1%
17626.29 2
 
< 0.1%
1068.21 2
 
< 0.1%
24791.24 2
 
< 0.1%
2336.99 2
 
< 0.1%
18599.36 2
 
< 0.1%
9077.41 2
 
< 0.1%
2588.04 2
 
< 0.1%
9019.39 2
 
< 0.1%
2431.38 2
 
< 0.1%
Other values (9971) 9980
99.8%
ValueCountFrequency (%)
0.04 1
< 0.1%
1.01 1
< 0.1%
1.49 1
< 0.1%
2.9 1
< 0.1%
3.01 1
< 0.1%
3.34 1
< 0.1%
3.37 1
< 0.1%
4 1
< 0.1%
4.81 1
< 0.1%
5.45 1
< 0.1%
ValueCountFrequency (%)
287527.83 1
< 0.1%
287025.82 1
< 0.1%
262980.5 1
< 0.1%
260097.13 1
< 0.1%
258423.83 1
< 0.1%
256676.9 1
< 0.1%
253372.83 1
< 0.1%
245475.96 1
< 0.1%
243072.04 1
< 0.1%
241826.04 1
< 0.1%

Platform
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size610.2 KiB
Coinbase
1515 
KuCoin
1484 
Huobi
1425 
Kraken
1419 
FTX
1404 
Other values (2)
2753 

Length

Max length8
Median length7
Mean length5.4714
Min length3

Characters and Unicode

Total characters54714
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKuCoin
2nd rowBinance
3rd rowOKX
4th rowFTX
5th rowKraken

Common Values

ValueCountFrequency (%)
Coinbase 1515
15.2%
KuCoin 1484
14.8%
Huobi 1425
14.2%
Kraken 1419
14.2%
FTX 1404
14.0%
Binance 1395
14.0%
OKX 1358
13.6%

Length

2025-05-24T20:08:01.277201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T20:08:01.310548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
coinbase 1515
15.2%
kucoin 1484
14.8%
huobi 1425
14.2%
kraken 1419
14.2%
ftx 1404
14.0%
binance 1395
14.0%
okx 1358
13.6%

Most occurring characters

ValueCountFrequency (%)
n 7208
13.2%
i 5819
10.6%
o 4424
 
8.1%
a 4329
 
7.9%
e 4329
 
7.9%
K 4261
 
7.8%
C 2999
 
5.5%
b 2940
 
5.4%
u 2909
 
5.3%
X 2762
 
5.0%
Other values (9) 12734
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37706
68.9%
Uppercase Letter 17008
31.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7208
19.1%
i 5819
15.4%
o 4424
11.7%
a 4329
11.5%
e 4329
11.5%
b 2940
7.8%
u 2909
7.7%
s 1515
 
4.0%
r 1419
 
3.8%
k 1419
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
K 4261
25.1%
C 2999
17.6%
X 2762
16.2%
H 1425
 
8.4%
F 1404
 
8.3%
T 1404
 
8.3%
B 1395
 
8.2%
O 1358
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54714
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7208
13.2%
i 5819
10.6%
o 4424
 
8.1%
a 4329
 
7.9%
e 4329
 
7.9%
K 4261
 
7.8%
C 2999
 
5.5%
b 2940
 
5.4%
u 2909
 
5.3%
X 2762
 
5.0%
Other values (9) 12734
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 7208
13.2%
i 5819
10.6%
o 4424
 
8.1%
a 4329
 
7.9%
e 4329
 
7.9%
K 4261
 
7.8%
C 2999
 
5.5%
b 2940
 
5.4%
u 2909
 
5.3%
X 2762
 
5.0%
Other values (9) 12734
23.3%

Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size628.3 KiB
Failed
3353 
Pending
3325 
Completed
3322 

Length

Max length9
Median length7
Mean length7.3291
Min length6

Characters and Unicode

Total characters73291
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompleted
2nd rowFailed
3rd rowPending
4th rowCompleted
5th rowPending

Common Values

ValueCountFrequency (%)
Failed 3353
33.5%
Pending 3325
33.2%
Completed 3322
33.2%

Length

2025-05-24T20:08:01.344242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T20:08:01.372837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
failed 3353
33.5%
pending 3325
33.2%
completed 3322
33.2%

Most occurring characters

ValueCountFrequency (%)
e 13322
18.2%
d 10000
13.6%
i 6678
9.1%
l 6675
9.1%
n 6650
9.1%
F 3353
 
4.6%
a 3353
 
4.6%
P 3325
 
4.5%
g 3325
 
4.5%
C 3322
 
4.5%
Other values (4) 13288
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63291
86.4%
Uppercase Letter 10000
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13322
21.0%
d 10000
15.8%
i 6678
10.6%
l 6675
10.5%
n 6650
10.5%
a 3353
 
5.3%
g 3325
 
5.3%
o 3322
 
5.2%
m 3322
 
5.2%
p 3322
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
F 3353
33.5%
P 3325
33.2%
C 3322
33.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 73291
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13322
18.2%
d 10000
13.6%
i 6678
9.1%
l 6675
9.1%
n 6650
9.1%
F 3353
 
4.6%
a 3353
 
4.6%
P 3325
 
4.5%
g 3325
 
4.5%
C 3322
 
4.5%
Other values (4) 13288
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13322
18.2%
d 10000
13.6%
i 6678
9.1%
l 6675
9.1%
n 6650
9.1%
F 3353
 
4.6%
a 3353
 
4.6%
P 3325
 
4.5%
g 3325
 
4.5%
C 3322
 
4.5%
Other values (4) 13288
18.1%

Wallet_Type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size650.7 KiB
Hot Wallet
2064 
Exchange Wallet
2018 
Ledger
1983 
Trezor
1971 
Cold Wallet
1964 

Length

Max length15
Median length11
Mean length9.6238
Min length6

Characters and Unicode

Total characters96238
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrezor
2nd rowCold Wallet
3rd rowHot Wallet
4th rowExchange Wallet
5th rowExchange Wallet

Common Values

ValueCountFrequency (%)
Hot Wallet 2064
20.6%
Exchange Wallet 2018
20.2%
Ledger 1983
19.8%
Trezor 1971
19.7%
Cold Wallet 1964
19.6%

Length

2025-05-24T20:08:01.396693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T20:08:01.425383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
wallet 6046
37.7%
hot 2064
 
12.9%
exchange 2018
 
12.6%
ledger 1983
 
12.4%
trezor 1971
 
12.3%
cold 1964
 
12.2%

Most occurring characters

ValueCountFrequency (%)
l 14056
14.6%
e 14001
14.5%
t 8110
8.4%
a 8064
8.4%
6046
 
6.3%
W 6046
 
6.3%
o 5999
 
6.2%
r 5925
 
6.2%
g 4001
 
4.2%
d 3947
 
4.1%
Other values (10) 20043
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74146
77.0%
Uppercase Letter 16046
 
16.7%
Space Separator 6046
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 14056
19.0%
e 14001
18.9%
t 8110
10.9%
a 8064
10.9%
o 5999
8.1%
r 5925
8.0%
g 4001
 
5.4%
d 3947
 
5.3%
n 2018
 
2.7%
c 2018
 
2.7%
Other values (3) 6007
8.1%
Uppercase Letter
ValueCountFrequency (%)
W 6046
37.7%
H 2064
 
12.9%
E 2018
 
12.6%
L 1983
 
12.4%
T 1971
 
12.3%
C 1964
 
12.2%
Space Separator
ValueCountFrequency (%)
6046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90192
93.7%
Common 6046
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 14056
15.6%
e 14001
15.5%
t 8110
9.0%
a 8064
8.9%
W 6046
 
6.7%
o 5999
 
6.7%
r 5925
 
6.6%
g 4001
 
4.4%
d 3947
 
4.4%
H 2064
 
2.3%
Other values (9) 17979
19.9%
Common
ValueCountFrequency (%)
6046
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 14056
14.6%
e 14001
14.5%
t 8110
8.4%
a 8064
8.4%
6046
 
6.3%
W 6046
 
6.3%
o 5999
 
6.2%
r 5925
 
6.2%
g 4001
 
4.2%
d 3947
 
4.1%
Other values (10) 20043
20.8%
Distinct731
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2023-02-28 09:51:58.222424
Maximum2025-02-27 09:51:58.222424
2025-05-24T20:08:01.457308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:01.492468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-05-24T20:08:00.306535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.275882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.557823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.735989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.921997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.110564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.338412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.401600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.587137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.767811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.951851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.143940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.370028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.434035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.615673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.799080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.981494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.176867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.401643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.464708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.644950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.830379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.013045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.208844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.432790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.495533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.674057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.861544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.044404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.243343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.465692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.526132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.704201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:07:59.892615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.075983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-05-24T20:08:00.275200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2025-05-24T20:08:01.523743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Transaction_IDUser_IDAmountPrice_Per_UnitTotal_ValueTransaction_FeeCryptoTransaction_TypePlatformStatusWallet_Type
Transaction_ID1.000-0.006-0.0030.006-0.001-0.0000.0000.0150.0130.0000.012
User_ID-0.0061.0000.006-0.015-0.0040.0040.0000.0000.0070.0170.002
Amount-0.0030.0061.0000.0170.6740.5310.0000.0000.0080.0170.000
Price_Per_Unit0.006-0.0150.0171.0000.6740.5350.0000.0000.0000.0000.013
Total_Value-0.001-0.0040.6740.6741.0000.7950.0000.0140.0150.0000.000
Transaction_Fee-0.0000.0040.5310.5350.7951.0000.0000.0150.0090.0000.000
Crypto0.0000.0000.0000.0000.0000.0001.0000.0000.0150.0130.017
Transaction_Type0.0150.0000.0000.0000.0140.0150.0001.0000.0080.0080.005
Platform0.0130.0070.0080.0000.0150.0090.0150.0081.0000.0140.000
Status0.0000.0170.0170.0000.0000.0000.0130.0080.0141.0000.000
Wallet_Type0.0120.0020.0000.0130.0000.0000.0170.0050.0000.0001.000

Missing values

2025-05-24T20:08:00.514767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-24T20:08:00.567358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Transaction_IDUser_IDCryptoTransaction_TypeAmountPrice_Per_UnitTotal_ValueTransaction_FeePlatformStatusWallet_TypeTransaction_Date
043446531394BitcoinWithdraw57.69514545248.782610634.92114990.70KuCoinCompletedTrezor2024-04-10 09:51:58.222424
190539739627SolanaStake27.70898248307.431338549.7134560.11BinanceFailedCold Wallet2023-10-04 09:51:58.222424
245129311506CardanoBuy80.36897744428.823570698.8138312.76OKXPendingHot Wallet2024-11-15 09:51:58.222424
384124984005XRPStake83.9004838755.31734574.741396.89FTXCompletedExchange Wallet2024-02-11 09:51:58.222424
476268666706DogecoinStake52.52836858667.263081695.4219087.58KrakenPendingExchange Wallet2023-09-15 09:51:58.222424
592538933284CardanoSell96.42320414327.751381527.5640679.41KrakenPendingTrezor2023-10-28 09:51:58.222424
616022766819BNBTransfer36.25144613832.81501459.3613192.99KrakenFailedCold Wallet2024-11-29 09:51:58.222424
768707683903XRPTransfer31.38778025214.70791433.4633678.05FTXPendingTrezor2024-08-01 09:51:58.222424
855605276299PolkadotWithdraw94.77152150095.834747658.00182900.88CoinbaseCompletedExchange Wallet2024-04-09 09:51:58.222424
912698954167DogecoinTransfer14.2497411890.2926936.14718.12KrakenPendingCold Wallet2024-02-16 09:51:58.222424
Transaction_IDUser_IDCryptoTransaction_TypeAmountPrice_Per_UnitTotal_ValueTransaction_FeePlatformStatusWallet_TypeTransaction_Date
999023886068678LitecoinBuy29.27089135891.841050586.1434360.45KuCoinCompletedTrezor2024-08-11 09:51:58.222424
999173923819592LitecoinSell30.47629816586.58505497.5513343.05CoinbaseCompletedTrezor2024-04-01 09:51:58.222424
999225404974281PolkadotBuy81.9507681600.83131189.25865.88BinancePendingLedger2025-02-04 09:51:58.222424
999317536997828CardanoStake62.03368153179.393298913.32107566.17OKXCompletedExchange Wallet2024-05-01 09:51:58.222424
999449786067252AvalancheStake94.42651228850.812724281.3621093.54KuCoinFailedHot Wallet2023-08-08 09:51:58.222424
999518699893233BitcoinStake30.91804720067.21620438.945603.47FTXPendingTrezor2023-02-28 09:51:58.222424
999633714444181CardanoBuy80.09084942488.693402955.2571081.12KrakenFailedLedger2024-08-28 09:51:58.222424
999794665522496BNBWithdraw19.00592136513.00693963.1929738.11CoinbaseCompletedLedger2023-04-06 09:51:58.222424
999815392172835LitecoinBuy11.87266819748.07234462.2810107.57OKXFailedTrezor2023-11-01 09:51:58.222424
999976072743815LitecoinTransfer80.5346377400.97596034.4323196.39CoinbasePendingExchange Wallet2024-10-10 09:51:58.222424